Parameter-Efficient Fine-Tuning for Zero-Shot Cross-Lingual Transfer in Low-Resource Turkic Languages on XCOPA and XNLI

Large language models (LLMs) have transformed natural language processing, yet their capabilities remain uneven across languages. Most multilingual models are trained primarily on high-resource languages, leaving many languages with large speaker populations underrepresented in both training data and evaluation benchmarks. This imbalance is particularly visible in the Turkic language family. This paper proposes a theoretical framework for studying cross-lingual transfer and parameter-efficient adaptation of multilingual LLMs within the Turkic language family, focusing on Azerbaijani, Kazakh, U

Research goal: How does parameter-efficient fine-tuning impact zero-shot cross-lingual transfer accuracy for low-resource Turkic languages on the XCOPA and XNLI benchmarks compared to full-model fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.